Interactive test-schedule adjustment method

ABSTRACT

A method for interactive test-schedule adjustment is provided. The steps include: displaying a table including the importance and period of each test item; displaying a first optimum schedule obtained from an optimal solution based on values entered by a user into the table, the schedule including a use schedule of each test facilities used for each of the multiple test items; displaying a second optimum schedule obtained from an optimal solution recalculated by mathematical programming after a use schedule of a test item the importance of which is changed on the first optimum schedule is changed; and displaying a third optimum schedule based on neighborhood solutions obtained by a search method using a history of optimal solutions if no agreement between the users is obtained on the second schedule.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. §371 from PCTApplication PCT/JP2014/073514, filed on Sep. 5, 2014, which claimspriority from Japanese Patent Application No. 2013-228854 filed Nov. 2,2013. The entire contents of both applications are incorporated hereinby reference.

FIELD OF THE INVENTION

The present invention relates to an interactive test-schedule adjustmentmethod, and more specifically to a method of creating/presenting a testschedule plan satisfactory to multiple users.

BACKGROUND

In the manufacturing industry, the optimization of a productmanufacturing schedule or a prototype test schedule is important toreduce/suppress the period and costs involved in the production or theperiod and costs involved in testing. Further, in the manufacture ofautomobiles, there are many test items such as prototypes. Since eachtest item is performed in a different test sector (hereinafter alsocalled a “user”), there is a need to optimize (adjust) a test scheduleto satisfy requirements from multiple test sectors.

As a tool available for the optimization, for example, there isoptimization software using mathematical programming such as CPLEX(registered trademark). Japanese Patent Application Publication no.2007-148635 discloses a production scheduling method for products usingoptimization calculation by mathematical programming. Japanese PatentApplication Publication no. 2013-143030 discloses an operation schedulepreparation method of steel making processes using optimizationcalculation by mathematical programming after a process having highoccupancy of a processing facility is set as a bottleneck process.

However, in conventional scheduling methods including the methodsdisclosed in Japanese Patent Application Publications no. 2007-148635and no. 2013-143030, the optimization calculation is mainly used toassign an item to be processed in time to each manufacturing resource,and is not always sufficient to optimize (adjust) a test schedule tosatisfy the requirements from multiple test sectors mentioned above. Inother words, since it is difficult for the conventional methods torepresent an objective function and constraint conditions satisfactoryto test sectors, it is difficult to create/present a test schedule plansatisfactory to multiple users.

SUMMARY OF THE INVENTION Technical Problems

Therefore, it is an object of the present invention to provide aninteractive test-schedule adjustment method, and more specifically toprovide a method of creating/presenting a test schedule plansatisfactory to multiple users.

Solution to Problems

The present invention provides an interactive test-schedule adjustmentmethod using a computer. The method includes the steps of:displaying atable including the importance and a test period of each of multipletest items for which adjustment of a test schedule is required;displaying a first optimum schedule obtained from an optimal solutioncalculated by mathematical programming based on values of the importanceand the test period entered by a user into the table, the first optimumschedule including a use schedule of each of test facilities used foreach of the multiple test items; if at least one or more of theimportance values in the table are changed, displaying a second optimumschedule obtained from an optimal solution recalculated by mathematicalprogramming after a use schedule of a test item the importance of whichis changed on the first optimum schedule is changed; and if no agreementbetween the users is obtained even on the second optimum schedule,displaying a third optimum schedule based on neighborhood solutionsobtained by a search method using a history of optimal solutions.

According to the method of the present invention, since not only theoptimum schedules obtained from optimal solutions calculated bymathematical programming, but also the optimum schedule based onneighborhood solutions obtained by the search method using the historyof the optimal solutions are displayed, an agreement between the usersis obtained/more easily obtained, so that the use schedule of each ofthe test facilities used can be optimized/adjusted.

In one aspect of the present invention, the neighborhood solutions bythe search method are obtained as discrete solutions of candidatesobtained by performing at least one of operations selected from amongreflection, expansion, and contraction using a history of three optimalsolutions.

In another aspect of the present invention, the step of displaying thesecond optimum schedule is executed each time the importance value ischanged and for each change pattern of a use schedule of a test item theimportance of which is changed.

In still another aspect of the present invention, when no agreementbetween the users is obtained even on the third optimum scheduleobtained after execution a predetermined number of times in the step ofdisplaying the third optimum schedule, a step of displaying a fourthoptimum schedule based on a Nash equilibrium solution is furtherincluded.

In yet another aspect of the present invention, at least one of thesteps of displaying the first, second, and third optimum schedulesincludes a step of highlighting a test item that becomes a bottleneckamong the multiple test items in the table displayed in the step ofdisplaying the table.

In still another aspect of the present invention, the step ofhighlighting a test item that becomes a bottleneck includes a step ofdisplaying, as a bottleneck facility, a test facility used in performingthe test item.

In yet another aspect of the present invention, the method furtherincludes a step of displaying a fifth optimum schedule when thebottleneck facility is built more for the multiple test items.

BRIEF DESCRIPTIONS OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagram showing a configuration example of a system forcarrying out a method of the present invention.

FIG. 2 is a block diagram showing a configuration example of a computerfor executing the method of the present invention.

FIG. 3 is a chart showing a flow of the method of the present invention.

FIG. 4 is a diagram showing an example of input tables of the presentinvention.

FIG. 5 is a diagram showing another example of input tables of thepresent invention.

FIG. 6 is a diagram showing a display example of a first optimumschedule of the present invention.

FIG. 7 is a diagram showing a display example of bottleneck informationof the present invention.

FIG. 8 is a diagram showing an example of change input of importance inthe present invention.

FIG. 9 is a diagram showing a display example of a second optimumschedule of the present invention.

FIG. 10 contains illustrations for describing a procedure for obtainingneighborhood discrete solutions by a search method of the presentinvention.

FIG. 11 is a diagram showing a display example of a schedule when afacility is extended in the present invention.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the present invention will be described with referenceto the accompanying drawings. FIG. 1 is a diagram showing aconfiguration example of a system for carrying out a method of thepresent invention. A system 100 includes a host (server) 10, PCs(terminals) 20, and a network drive 30 connected through a network 40 tobe communicable with each other. The network drive 30 includes storagemeans such as an HDD or a tape drive. Although the host (server) 10 andthe network drive 30 are each illustrated as one component and the twoPCs (terminals) 20 are illustrated in FIG. 1, this is just an example.It goes without saying that any number (one, or two or more) of machinescan be included as each component. Particularly, the PCs (terminals) 20are installed according to the number of test sectors (hereinafter alsocalled users).

A method of the present invention, the details of which will bedescribed later, is carried out in the configuration of FIG. 1 in such amanner, for example, that the PCs 20 call and execute softwaredownloaded from the server 10 or the network drive 30, or softwarestored in storage means (HDD or the like) incorporated therein.

FIG. 2 is a block diagram showing a configuration example of a computerfor executing the method of the present invention. FIG. 2 illustratesthe configuration example of each PC 20 in FIG. 1. The PC 20 includes aprocessor (CPU) 200, storage means 210, and various I/F 220, which areconnected to each other through a bus 230. The various I/F 220 is usedas a generic term that includes an input I/F, an output I/F, an externalstorage I/F, an external communication I/F, and the like. Each I/F isconnected to corresponding means, namely input means 240 such as akeyboard and a mouse, display means 250 such as a CRT or an LCD, orexternal storage means 260 such as a semiconductor memory through a USBconnection or an HDD. The storage means 210 can include semiconductormemories such as a RAM and a ROM, and an HDD. The content of eachdisplay step of the method of the present invention is displayed on thedisplay means 250.

FIG. 3 is a chart showing a flow of the method of the present invention.In step S11, an input table on each of multiple items, for whichadjustment of a test schedule is required, is displayed. The input tablecontains the importance and test period of each of test items (each kindof test), and a necessary measurement function. Each of tables includingthis input table is displayed on the display means 250.

FIG. 4 and FIG. 5 show examples of input tables displayed. FIG. 4 is anexample of display to test sector 1, and FIG. 5 shows an example ofdisplay to test sector 2. Illustrated in FIG. 4 are an input tableindicative of the importance and test period of each of three test itemsA, B, and C, and an input table indicative of measurement functions 1 to5. Numeric values in the tables are numeric values already input fromsector 1 after each table is displayed. In the table before input, theblank as the initial state or default values (the previous values or thelike) in each sector are indicated.

The numeric values of importance are set to come to 100 in total. Thelarger the numeric value, the higher the importance. The test periodmeans a period (desired time) that can be allocated for each test, andcan be set in an arbitrary unit, such as time, day, week, or month,depending on the test target/item. Any other item such as release timecan also be added in addition to the test period. Numeric value 1 of themeasurement functions 1 to 5 means that the measurement function isrequired for the test, and the blank means that the measurement functionis not required. The display of “scheduling” in FIG. 4 denotes an iconto be clicked to make a schedule adjustment request after data (numericvalues) are input into the table.

The meanings of the tables in FIG. 5 are basically the same as those inFIG. 4. In FIG. 5, different points from FIG. 4 are that the test itemsare D, E, and F, and that there is a measurement function 6 instead ofthe measurement function 2. Thus, the input tables are displayed foreach test sector. The number and contents (kinds) of test items, and thenumber and contents (kinds) of measurement functions in the tables canbe set arbitrarily for each test sector or each test target.

When information (data) on the above input tables is input from eachsector, an available test facility and processing time for each testitem are automatically derived based on facility information possessedby a supervisor. The supervisor holds (stores), as a table (not shown),the derived available facility and processing time for each test item.Further, the supervisor holds a value (priority) of a priority ratio(100 in total) for each test sector in advance. For example, in theexamples of FIG. 4 FIG. 5, test sector 1 is set to 40 and test sector 2is set to 60, and a schedule is so adjusted that requirements from testsector 2 will be given higher priority than requirements from testsector 1 in displaying (calculating) the schedule to be described later.

Returning to FIG. 3, a first optimum schedule is displayed in step S12in response to receiving a click (request) on the “scheduling” icon inFIG. 4 and FIG. 5. Here, the first optimum schedule means a firstschedule obtained from an optimal solution calculated by mathematicalprogramming for the use schedule of each of test facilities used foreach of multiple test items based on values of importance and testperiod entered by the user into the input table. The optimal solutioncalculated by mathematical programming can be obtained, for example, asan optimal solution by mixed integer programming. The optimal solutionby mixed integer programming can be obtained by determining a useschedule (allocation) of a test facility that satisfies the followingformula (1) under predetermined constraint conditions (for example,including the following formulas (2) and (3)):

-   [Math. 1]

$\begin{matrix}{{{{minimize}{\sum\limits_{j = 1}^{n}\; {w_{j}U_{j}}}} + {\rho {\sum\limits_{j = 1}^{n}\; {w_{j}\left( {C_{j} - d_{j}} \right)}}}}{U_{j} = \left\{ {{\begin{matrix}{1\left( {{{if}\mspace{14mu} C_{j}} \geq d_{j}} \right)} \\{0({otherwise})}\end{matrix}\mspace{14mu} C_{j}} = {\sum\limits_{k = 1}^{n}\; {\sum\limits_{l = 1}^{m}\; {\left( {i_{kl} + p_{jl}} \right)u_{jkl}}}}} \right.}} & (1)\end{matrix}$

Constraint Condition 1: (parallel performing disabled)

-   [Math. 2]

$\begin{matrix}{{\sum\limits_{k = 1}^{n}\; {\sum\limits_{l = 1}^{m}\; u_{jkl}}} = {{1\mspace{14mu} {\forall j}} = {1\mspace{14mu} \ldots \mspace{14mu} n}}} & (2)\end{matrix}$

Constraint Condition 2: (exclusive use)

-   [Math. 3]

$\begin{matrix}{{{{\sum\limits_{j = 1}^{n}\; u_{jkl}} \leq {1\mspace{14mu} {\forall k}}} = {1\mspace{14mu} \ldots \mspace{14mu} n}},{{\forall l} = {1\mspace{14mu} \ldots \mspace{14mu} m}}} & (3)\end{matrix}$

In the formulas (1) to (3), design variables and parameters have thefollowing meanings:

<Design Variable>

-   -   u_(jkl): 1 when test j is the k-th to be performed in facility        I, or otherwise 0.    -   U_(j): The presence or absence of performing test j. 1 when test        j is not performed, or otherwise 0.    -   t_(kl): The starting time of the k-th test in facility I.

<Parameter>

-   -   w_(j): The weight of test j (the value of test j).    -   p_(jl): The time required to process test j in facility I.    -   d_(j): The target deadline of test j.

In formula (1), the objective function is set as the quantity of testresults within a fixed period to evaluate, as optimum, an allocationthat maximizes the quantity. However, since performing the test is setas U_(j)=0, minimize is obtained as in formula (1). The weight w_(j) oftest j is determined by dividing the above-mentioned priority valuepossessed by the supervisor by an importance value of the test from eachsector. Further, an item for giving a penalty to a test that exceeds thetest period is added as an arbitrary coefficient p to find a combinationthat finishes the test as quickly as possible.

FIG. 6 shows a display example of the first optimum schedule obtained instep S12. FIG. 6 shows use schedules of test facilities F1 to F3 in Xmonth for test items A to E in FIG. 4 and FIG. 5. In FIG. 6, the lengthof a character string “XXX” indicates the period (days) in which testitem X uses/occupies test facility Y. For example, it means that testfacility F3 is used for test item D for a period of “DDD” (a few days)at the beginning of the month. Note that the indication in FIG. 6 isjust an example, and any indication format for representing a periodsuch as an arrow “⇄” or a straight line “-” can be selected instead ofthe character string “XXX.”

A “bottleneck analysis” icon in FIG. 6 is displayed to be clicked whenthe user wants bottleneck analysis. Here, the bottleneck analysis meansthat test items and test facilities that become bottlenecks (critical)in determining an optimum schedule are extracted. In the bottleneckanalysis, for example, a test that falls under any of the following (a)to (e) is determined to be a bottleneck test:

-   -   (a)A test having a large fluctuation range in upper/lower limit        analysis or the like based on normal form of mathematical        programming;    -   (b) A test high in the frequency of facility usage;    -   (c) A test small in the difference between both sides of an        inequality as a constraint condition;    -   (d) A test having a test item capable of being processed earlier        and existing in any other facility; and    -   (e) A test detected as a combination of (a) to (d).

Then, a facility in which the above test is performed is determined tobe a bottleneck facility, and importance values are added up to sethigher bottleneck tests in order from the largest value.

As a result of the bottleneck analysis, the tests and facilitiesdetermined to be bottlenecks are highlighted. For example, the testitems are displayed in color or highlighted with blinking or the like inthe tables of FIG. 4 and FIG. 5. In the display example of the optimumschedule in FIG. 6, the bottleneck facilities are highlighted in thesame manner. Further, for example, in the examples of FIG. 4 to FIG. 6,bottleneck information as shown in FIG. 7 is displayed as supervisorinformation. In FIG. 7, the bottleneck facilities are F2 and F1 and thebottleneck tests are F, B, and E, and the rankings are best to worstfrom top to bottom. Then, each sector is informed whether a requestedtest becomes a bottleneck and of a rough ranking thereof.

Returning to FIG. 3, it is determined in step S13 whether it is agreedon the first optimum schedule. Specifically, for example, thedetermination is made by determining whether the importance is changedin the input table illustrated in FIG. 4 or FIG. 5. For example, when anumeric value larger than the importance is input, it is determined thatthe test sector (user) does not satisfy the schedule (facility useschedule) of the test item, i.e., that the test sector is requestingspeeding up the schedule.

An example of change input of importance is shown in FIG. 8. FIG. 8shows an example where test sector 1 increases the importance of testitem B from 40 (FIG. 4) to 45, and conversely, decreases the importanceof test item C from 40 to 35. As mentioned above, the numeric values ofimportance are set to be 100 in total. Therefore, for example, when sucha change that the importance of test item B is only increased to 45 isinput, an error message can be displayed to urge the user to change anumeric value so that the total will become 100.

When the determination in step S13 is Yes (agreed), scheduling by themethod is ended. When this determination is No (importance changed=notagreed), a second optimum schedule is displayed in the following stepS14. Here, the second optimum schedule means a second schedule obtainedfrom an optimal solution calculated by mathematical programming for theuse schedule of each of test facilities illustrated in FIG. 6 based onthe changed value of importance entered by the user into the inputtable. In other words, rescheduling is performed.

When the case of FIG. 4 to FIG. 6 is taken as an example, therescheduling in step S14 is performed, for example, in the followingprocedure: A schedule pattern in which a test period “BB . . . BB” oftest item B the importance of which is increased in FIG. 8 is replacedwith all test periods (“AAA” and “EE . . . EE”) that precede in thefacility F1 is prepared. Conversely, if the importance is decreased, allschedule patterns with the test period postponed are prepared. Then, anoptimal solution is obtained by mixed integer programming using formula(1) in the same manner as in step S12. Note that schedule patterns thatdo not satisfy the above-mentioned constraint conditions (formulas (2),(3), and the like) are excluded.

FIG. 9 shows a display example of the second optimum schedule. Thecontent (meaning of each item) of the display example in FIG. 9 is thesame as that of the display example in FIG. 6. A point changed from thecase of FIG. 6 is that the test period “BB . . . BB” of test item B ismade earlier than the test period “EE . . . EE” of test item E on theschedule of test facility F1, reflecting the increase in the importanceof test item B. Thus, the schedule of a corresponding test facility isautomatically changed and displayed according to a change in importance.

In the following step S15, it is determined whether it is agreed on thesecond optimum schedule. A specific procedure is the same as that instep S12. When the determination in step S15 is Yes (agreed), schedulingby the method is ended. When this determination is No (importancechanged=not agreed), a third optimum schedule is displayed in thefollowing step S16. The third optimum schedule means a schedule with animportance change reflected, like the case of the second optimumschedule in step S14.

Like the first and second optimum schedules, the third optimum schedulecan be obtained by the method of obtaining an optimal solution by mixedinteger programming. Further, in the present invention, a third optimumschedule based on neighborhood solutions obtained by a search methodusing a history of optimal solutions can be derived and displayed torespond to a case where no agreement on the schedule obtained from theoptimal solution is reached. When no agreement on the optimal solutionis reached, since it can be considered that a solution satisfactory tousers exists around/near the optimal solution, the third optimumschedule based on the neighborhood solutions can be derived, forexample, by the following method.

FIG. 10 contains illustrations for describing a procedure for obtainingneighborhood solutions by the search method using the history of optimalsolutions, or more precisely, a procedure for obtaining neighborhooddiscrete solutions. FIGS. 10A, 10B, and 10C are examples of obtainingneighborhood discrete solutions by operations of “reflection,”“expansion,” “contraction” in this order in the downhill simplex method.In FIG. 10A to FIG. 10C, numbers 1 to 3 are points (positions)indicative of a history of optimal solutions, a point (position) ofnumber 4 is a solution candidate obtained by each operation, and points(positions) of numbers 5 to 9 are discrete solutions in the neighborhoodof the solution candidate. The history of optimal solutions 1 to 3denotes the order of appearance of optimal solutions. Using the solutionhistory of numbers 1 to 3, i.e., setting two points (1 and 2) as a base,the operation of reflection in FIG. 10A, the operation of expansion inFIG. 10B, and the operation of contraction in FIG. 10C are carried out,respectively, to create a new solution candidate (circled number 4).After that, discrete solutions (5 to 9) in the neighborhood of each ofthe solution candidates are created and displayed in order from theclosest point. In other words, the nearest discrete solution (5) isfirst displayed.

Each of the solution candidates (4) in FIG. 10A to FIG. 10D can beobtained, for example, by solving the following equations (4) to (6).

(a):

-   [Math. 4]

$\begin{matrix}{{x_{4\; r} = {\overset{\_}{x} + {\rho \left( {\overset{\_}{x} - x_{3}} \right)}}},{\overset{\_}{x} = {\sum\limits_{i = 1}^{3}\; {x_{i}/3}}},{\rho > 0}} & (4)\end{matrix}$

(b):

[Math. 5]

x _(4e) =x +χ(x _(4r) −x ), χ>ρ   (5)

(c):

[Math. 6]

x _(4c) =x −γ( x−x ₃), γ>0   (6)

Returning to FIG. 3, it is determined in step S17 whether it is agreedon the third optimum schedule. The determination method is the same asthat in step S13 and step S15. Specifically, for example, thedetermination is made by determining whether the importance is changedin the input table illustrated in FIG. 4 or FIG. 5. When thedetermination in step S17 is Yes (agreed), scheduling by the method isended. When this determination is No (importance changed=not agreed),the procedure returns to step S16 to repeat displaying of a fourthoptimum schedule and beyond until a total agreement is reached.

When an agreement on the schedule is reached in the above step,expectations when an investment in (extension of) a bottleneck facilityis made can be simulated to further improve test efficiency from theschedule. For example, when facility F2 in FIG. 7 is built more (F2+ isadded) on the schedule of FIG. 9, the period (FF . . . FF) of test itemF can be allocated to an added device (F2+) as shown in FIG. 11. As aresult, the total schedule can be shortened. In other words, a period of21 to 30 (days) can be made unnecessary as shown in FIG. 11, enablingfurther improvement in test efficiency.

The embodiment of the present invention has been described withreference to the accompanying drawings. However, the present inventionis not limited to the embodiment. The present invention can be carriedout in forms to which various improvements, corrections, andmodifications are added based on the knowledge of those skilled in theart without departing from the purpose of the present invention.

1. A non-transitory computer readable storage medium tangibly embodyinga computer readable program code having computer readable instructionswhich, when implemented cause a computer device to carry out the stepsof a method for an interactive test-schedule adjustment, the methodcomprising: displaying a table on a display, the table includingimportance and a test period of a plurality of test items for whichadjustment of a test schedule is required; displaying a first optimumschedule on the display, the first optimum schedule being obtained froman optimal solution calculated by mathematical programming based onvalues of the importance and the test period entered by a user into thetable, the first optimum schedule including a use schedule of each oftest facilities used for each of the plurality of test items; displayinga second optimum schedule on the display if at least one of theimportance values in the table are changed, the second optimum schedulebeing obtained from an optimal solution recalculated by mathematicalprogramming after a use schedule of a test item, the importance of whichis changed on the first optimum schedule, is changed; and displaying athird optimum schedule based on neighborhood solutions obtained by asearch method using a history of the optimal solutions on the display ifno agreement between the users on the second optimum schedule.
 2. Thenon-transitory computer readable storage medium according to claim 1,wherein the neighborhood solutions by the search method are obtained asdiscrete solutions of candidates obtained by performing at least one ofoperations selected from among reflection, expansion, and contractionusing a history of three of the optimal solutions.
 3. The non-transitorycomputer readable storage medium according to claim 1, wherein the stepof displaying the second optimum schedule is executed each time theimportance value is changed and for each change pattern of a useschedule of a test item the importance of which is changed.
 4. Thenon-transitory computer readable storage medium according to claim 2,wherein the step of displaying the third optimum schedule is executedwhile changing the discrete solutions of the candidates a predeterminedlimited number of times until an agreement between the users isobtained.
 5. The non-transitory computer readable storage mediumaccording to claim 4, the method further comprising: displaying a fourthoptimum schedule based on a Nash equilibrium solution, when no agreementbetween the users is obtained even on the third optimum scheduleobtained after execution the predetermined limited number of times inthe step of displaying the third optimum schedule.
 6. The non-transitorycomputer readable storage medium according to claim 1, wherein at leastone of the steps of displaying the first, second, and third optimumschedules includes a step of highlighting a test item that becomes abottleneck among the plurality of test items in the table displayed inthe step of displaying the table.
 7. The non-transitory computerreadable storage medium according to claim 6, wherein the step ofhighlighting a test item that becomes a bottleneck includes a step ofdisplaying, as a bottleneck facility, a test facility used in performingthe test item.
 8. The computer readable storage medium according toclaim 7, the method further comprising: displaying a fifth optimumschedule when the bottleneck facility is built more for the plurality oftest items.
 9. (canceled)
 10. A computer-implemented method forinteractive test-schedule adjustment the method comprising: displaying atable on a display, the table including importance and a test period ofa plurality of test items for which adjustment of a test schedule isrequired; displaying a first optimum schedule on the display, the firstoptimum schedule being obtained from an optimal solution calculated bymathematical programming based on values of the importance and the testperiod entered by a user into the table, the first optimum scheduleincluding a use schedule of each of test facilities used for each of theplurality of test items; displaying a second optimum schedule on thedisplay if at least one of the importance values in the table arechanged, the second optimum schedule being obtained from an optimalsolution recalculated by mathematical programming after a use scheduleof a test item, the importance of which is changed on the first optimumschedule, is changed; and displaying a third optimum schedule based onneighborhood solutions obtained by a search method using a history ofthe optimal solutions on the display if no agreement between the userson the second optimum schedule.
 11. The computer-implemented methodaccording to claim 10, wherein the neighborhood solutions by the searchmethod are obtained as discrete solutions of candidates obtained byperforming at least one of operations selected from among reflection,expansion, and contraction using a history of three of the optimalsolutions.
 12. The computer-implemented method according to claim 10,wherein the step of displaying the second optimum schedule is executedeach time the importance value is changed and for each change pattern ofa use schedule of a test item the importance of which is changed. 13.The computer-implemented method according to claim 11, wherein the stepof displaying the third optimum schedule is executed while changing thediscrete solutions of the candidates a predetermined limited number oftimes until an agreement between the users is obtained.
 14. Thecomputer-implemented method according to claim 13, the method furthercomprising: displaying a fourth optimum schedule based on a Nashequilibrium solution, when no agreement between the users is obtainedeven on the third optimum schedule obtained after execution thepredetermined limited number of times in the step of displaying thethird optimum schedule.
 15. The computer-implemented method according toclaim 10, wherein at least one of the steps of displaying the first,second, and third optimum schedules includes a step of highlighting atest item that becomes a bottleneck among the plurality of test items inthe table displayed in the step of displaying the table.
 16. Thecomputer-implemented method according to claim 15, wherein the step ofhighlighting a test item that becomes a bottleneck includes a step ofdisplaying, as a bottleneck facility, a test facility used in performingthe test item.
 17. The computer-implemented method according to claim16, the method further comprising: displaying a fifth optimum schedulewhen the bottleneck facility is built more for the plurality of testitems.
 18. A system for carrying out a method for interactivetest-schedule adjustment, the system comprising: a memory; a processorcommunicatively coupled to the memory; and a test-schedule adjustmentmodule communicatively coupled to the memory and the processor, whereinthe test-schedule adjustment module is configured to perform the stepsof a method comprising: displaying a table on a display, the tableincluding importance and a test period of a plurality of test items forwhich adjustment of a test schedule is required; displaying a firstoptimum schedule on the display, the first optimum schedule beingobtained from an optimal solution calculated by mathematical programmingbased on values of the importance and the test period entered by a userinto the table, the first optimum schedule including a use schedule ofeach of test facilities used for each of the plurality of test items;displaying a second optimum schedule on the display if at least one ofthe importance values in the table are changed, the second optimumschedule being obtained from an optimal solution recalculated bymathematical programming after a use schedule of a test item, theimportance of which is changed on the first optimum schedule, ischanged; and displaying a third optimum schedule based on neighborhoodsolutions obtained by a search method using a history of the optimalsolutions on the display if no agreement between the users on the secondoptimum schedule.
 19. The system according to claim 18, wherein theneighborhood solutions by the search method are obtained as discretesolutions of candidates obtained by performing at least one ofoperations selected from among reflection, expansion, and contractionusing a history of three of the optimal solutions.
 20. The systemaccording to claim 18, wherein the step of displaying the second optimumschedule is executed each time the importance value is changed and foreach change pattern of a use schedule of a test item the importance ofwhich is changed.
 21. The system according to claim 19, wherein the stepof displaying the third optimum schedule is executed while changing thediscrete solutions of the candidates a predetermined limited number oftimes until an agreement between the users is obtained.